Map indicates the layout of the F1 generation resulting from a cross between EC201 and EC103 parents. Column 1 is approximately lengthways facing north.
“Map of F1 Trees”
Diagram indicates the areas of leaf collection regarding height. Each Tree had 10 leaves collected, 3 from Low, 4 from Mid and 3 from High. The first leaf sampled was measured twice for replication comparison.
“Diagram of leaf collection levels”
“Full.xlsx” contains measurement information from sampling with the Dualex (https://www.force-a.com/en/capteurs-optiques-optical-sensors/dualex-scientific-chlorophyll-meter/), including;
Surface content of chlorophyll in ?g/cm? (Chl)
Epidermal Flavoid content in absorbance units; Flavonol(Flav) and Anthocyanin(Anth)
Nitrogen Balance Index status (NBI)
It also contains information about the block position, the leaf height information, and presense or absence of flowering
Sheet “Dup” contains only the samples that were replicated.
# Import Data Measures
Data <-read.xlsx("Full.xlsx", sheetName ="Full")
head(Data)
## Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 1 4 CG 0 0 0 20 OG CG N
## 2 4 CG 0 0 0 20 OG CG N
## 3 4 CG 0 0 0 20 OG CG N
## 4 4 CG 0 0 0 20 OG CG N
## 5 4 CG 0 0 0 20 OG CG N
## 6 4 CG 0 0 0 20 OG CG N
## measure Height Flower Chl Flav Anth NBI
## 1 9 H Y 28.220 2.418 0.205 11.67
## 2 3 L Y 27.958 2.298 0.691 12.17
## 3 11 H Y 33.727 2.458 0.527 13.72
## 4 4 L Y 25.938 1.758 0.172 14.76
## 5 10 H Y 36.205 2.283 0.270 15.86
## 6 6 M Y 34.332 2.115 0.150 16.23
Data$Column = as.factor(Data$Column)
Data$Row = as.factor(Data$Row)
# Import Replicate Data
Dup <-read.xlsx("Full.xlsx", sheetName ="Dup")
head(Dup)
## Collection.Day group Group.ID Tree.ID Rep. measure Height Chl Flav
## 1 2.0 23 60 IN4DV Y1 1 L 1.916 2.363
## 2 1.5 3 1 IN4BT Y1 1 L 3.124 2.300
## 3 2.0 17 54 IN4DL Y2 2 L 3.414 1.826
## 4 1.5 32 28 IN4CP Y2 2 L 4.097 1.943
## 5 2.0 9 46 IN4DC Y1 1 L 4.909 1.928
## 6 1.5 5 3 IN4BW Y1 1 L 4.924 1.848
## Anth NBI
## 1 0.174 0.81
## 2 0.191 1.36
## 3 0.112 1.87
## 4 0.051 2.11
## 5 0.103 2.55
## 6 0.165 2.66
#Isolate Crimson Glory Outgroup
CG = Data[c(1:11),]
#Isolate East Cape 201 Parent
EC201 = Data[c(12:21),]
#Isolate East Cape 103 Parent
EC103 = Data[c(22:33),]
#Isolate Offspring from the Parental Cross
F1 = Data[c(34:1825),]
Replicates were taken by measuring a single leaf sample from each tree twice, in order to establish consistency and reliability of measurements with the Dualex.
## Min. 1st Qu. Median Mean 3rd Qu.
## RepAnth 0.00100000 0.06500000 0.09850000 0.09799367 0.12525000
## RepChl 1.91600000 25.82475000 36.02700000 35.76269937 46.61000000
## RepFlav 1.05600000 1.76775000 1.99400000 1.96720570 2.19725000
## RepNBI 0.81000000 12.70250000 18.19000000 18.73398734 24.45750000
## Max.
## RepAnth 0.25400000
## RepChl 59.67400000
## RepFlav 2.71400000
## RepNBI 43.49000000
## # A tibble: 2 x 5
## Rep. Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Y1 0.0983 34.9 1.98 18.2
## 2 Y2 0.0977 36.6 1.95 19.2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##### Chlorphyll Replicate Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##### Flavonol Replicate Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##### Nitrogen Balance Replicate Plots
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
There is no statistically significant differences between the two groups of measurements, this is a good sign indicative of the accuracy of the Dualex.
## Analysis of Variance Table
##
## Response: Dup$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 218 218.33 1.033 0.3102
## Residuals 314 66366 211.36
## Analysis of Variance Table
##
## Response: Dup$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 0.0521 0.052129 0.6506 0.4205
## Residuals 314 25.1596 0.080126
## Analysis of Variance Table
##
## Response: Dup$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 0.00003 0.0000304 0.0151 0.9023
## Residuals 314 0.63303 0.0020160
## Analysis of Variance Table
##
## Response: Dup$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 82.5 82.530 1.115 0.2918
## Residuals 314 23241.0 74.016
The absence of statistically significant results indicates that our replicates are likely to be consistent.
Allocation refers to which group measurements were taken from, i.e. A Parental Tree (EC103 or EC201), Outgroup Tree (CG), Parental Offspring (F1)
## Min. 1st Qu. Median Mean 3rd Qu.
## AllAnth 0.00100000 0.06800000 0.09600000 0.09772877 0.12400000
## AllChl 0.13000000 24.10200000 36.21000000 35.21443342 46.82300000
## AllFlav 1.05600000 1.80600000 1.97800000 1.96440493 2.12800000
## AllNBI 0.07000000 12.14000000 18.64000000 18.34807123 24.44000000
## Max.
## AllAnth 0.69100000
## AllChl 59.90500000
## AllFlav 2.86100000
## AllNBI 49.17000000
## # A tibble: 4 x 5
## Allocation Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 CG 0.258 36.0 2.17 16.7
## 2 EC103 0.0588 52.6 1.60 33.1
## 3 EC201 0.0779 52.6 1.71 31.7
## 4 F1 0.0971 35.0 1.97 18.2
## Analysis of Variance Table
##
## Response: Data$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 6742 2247.21 9.9149 1.748e-06 ***
## Residuals 1821 412728 226.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Data$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 6742 2247.21 9.9149 1.748e-06 ***
## Residuals 1821 412728 226.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Data$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 2.713 0.90423 14.755 1.708e-09 ***
## Residuals 1821 111.598 0.06128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Data$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 0.3040 0.101343 47.474 < 2.2e-16 ***
## Residuals 1821 3.8873 0.002135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
F1 Samples (Approx 3 years) appear more similar to that of the Crimson Glory plant than either/both parents - this is possibly due to age effects as CG is likely more similar in this regard being shorter (No age confirmed). ANOVAs incidate there is significant differences between the allocations - this is to be expected.
## # A tibble: 2 x 5
## Tree.ID Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 EC103 0.0588 52.6 1.60 33.1
## 2 EC201 0.0779 52.6 1.71 31.7
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ParAnth 0.004000 0.048250 0.058000 0.067500 0.086750 0.156000
## ParChl 30.722000 50.356750 55.739000 52.598909 57.794750 59.757000
## ParFlav 1.293000 1.475000 1.594000 1.650273 1.821000 2.138000
## ParNBI 14.370000 29.872500 34.025000 32.481818 35.892500 44.880000
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Welch Two Sample t-test
##
## data: Parent$Anth by Parent$Tree.ID
## t = -1.2283, df = 19.107, p-value = 0.2342
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.05154327 0.01340994
## sample estimates:
## mean in group EC103 mean in group EC201
## 0.05883333 0.07790000
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Welch Two Sample t-test
##
## data: Parent$Chl by Parent$Tree.ID
## t = -0.003842, df = 17.804, p-value = 0.997
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.830954 7.802387
## sample estimates:
## mean in group EC103 mean in group EC201
## 52.59242 52.60670
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Welch Two Sample t-test
##
## data: Parent$Flav by Parent$Tree.ID
## t = -1.141, df = 19.612, p-value = 0.2676
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.31409194 0.09215861
## sample estimates:
## mean in group EC103 mean in group EC201
## 1.599833 1.710800
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Welch Two Sample t-test
##
## data: Parent$NBI by Parent$Tree.ID
## t = 0.47573, df = 14.7, p-value = 0.6413
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.076667 7.987334
## sample estimates:
## mean in group EC103 mean in group EC201
## 33.14333 31.68800
Interesting sample pattern here, Chl and NBI start low and work high, Flav does the opposite. Maybe accuracy of measurements?
Investigating measures across the F1 cross population
## # A tibble: 6 x 5
## Tree.ID Chl NBI Anth Flav
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 IN4BT 34.5 16.2 0.110 2.17
## 2 IN4BV 27.5 13.8 0.121 2.01
## 3 IN4BW 24.8 12.5 0.127 1.99
## 4 IN4BX 37.6 18.4 0.0905 2.05
## 5 IN4BY 24.2 12.5 0.110 1.98
## 6 IN4BZ 32.0 15.6 0.106 2.09
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 0.5253 0.0033247 1.8093 2.001e-08 ***
## Residuals 1633 3.0007 0.0018376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Boxplot Chlorophyll F1 FChl <- ggplot(F1) + aes(x = Tree.ID, y = Chl) + geom_boxplot() +geom_hline(yintercept = mean(F1\(Chl), color = "purple4") + ylab("Chlorophyll Measure") + xlab("Tree ID") + ggtitle("Chlorophyll of F1 Population") + theme_classic();FChl #Mean Measures Chlorophyll FSChl <- ggplot(F12) + aes(x = Tree.ID, y = Chl) + geom_point() +geom_hline(yintercept = mean(F12\)Chl)) + ylab(“Chlorophyll Measure”) + xlab(“Tree ID”) + ggtitle(“Chlorophyll of F1 population”) #Histogram of Chlorophyll FHChl <- ggplot(F1) + aes(x = Chl) + geom_histogram() #Display Chlorophyll Plots grid.arrange(FSChl, FHChl) #Chlorophyll ANOVA FCA = lm(F1\(Chl ~ F1\)Tree.ID) anova(FCA)
#Boxplot Flavanol F1 FFlav <- ggplot(F1) + aes(x = Tree.ID, y = Flav) + geom_boxplot() +geom_hline(yintercept = mean(F1\(Flav), color = "purple4") + ylab("Flavanol Measure") + xlab("Tree ID") + ggtitle("Flavonol of F1 Population") + theme_classic();FFlav #Mean Measures Flavonol FSFlav <- ggplot(F12) + aes(x = Tree.ID, y = Flav) + geom_point() +geom_hline(yintercept = mean(F12\)Flav)) + ylab(“Flavonol Measure”) + xlab(“Tree ID”) + ggtitle(“Flavonol of F1 population”) #Histogram of Flavonol FHFlav <- ggplot(F1) + aes(x = Flav) + geom_histogram() #Display Chlorophyll Plots grid.arrange(FSFlav, FHFlav) #Flavonol ANOVA FFA = lm(F1\(Flav ~ F1\)Tree.ID) anova(FFA)
#Boxplot NBI F1 FNBI <- ggplot(F1) + aes(x = Tree.ID, y = NBI) + geom_boxplot() +geom_hline(yintercept = mean(F1\(NBI), color = "purple4") + ylab("NBI Measure") + xlab("Tree ID") + ggtitle("NBI of F1 Population") + theme_classic();FNBI #Mean Measures NBI FSNBI <- ggplot(F12) + aes(x = Tree.ID, y = NBI) + geom_point() +geom_hline(yintercept = mean(F12\)NBI)) + ylab(“NBI Measure”) + xlab(“Tree ID”) + ggtitle(“NBI of F1 population”) #Histogram of NBI FHNBI <- ggplot(F1) + aes(x = NBI) + geom_histogram() #Display NBI Plots grid.arrange(FSNBI, FHNBI) #NBI ANOVA FNA = lm(F1\(NBI ~ F1\)Tree.ID) anova(FNA)
<img src="Analysis_files/figure-html/investigating measure numbers of random samples-1.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-2.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-3.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-4.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-5.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-6.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-7.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-8.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-9.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-10.png" width="672" />
#### Height Analysis
<img src="Analysis_files/figure-html/Height Analysis-1.png" width="672" />
#### Row Analysis
There appears to be no signficant differences between Rows with 3 trees and rows with 4 trees for any of the measures.
#### Row Plots and ANOVAs
<img src="Analysis_files/figure-html/unnamed-chunk-4-1.png" width="672" />
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-4-2.png" width="672" />
<img src="Analysis_files/figure-html/unnamed-chunk-4-3.png" width="672" />
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-4-4.png" width="672" />
<img src="Analysis_files/figure-html/unnamed-chunk-4-5.png" width="672" />
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-4-6.png" width="672" />
<img src="Analysis_files/figure-html/unnamed-chunk-4-7.png" width="672" />
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-4-8.png" width="672" />
#### Column Analysis
<img src="Analysis_files/figure-html/Column Analysis-1.png" width="672" />
Controlling for Height,Column,Row effects
#### Looking at controlled data
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-6-1.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-2.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-3.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-4.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-5.png" width="672" />
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-6-6.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-7.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-8.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-9.png" width="672" />
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-6-10.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-11.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-12.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-13.png" width="672" />
stat_bin() using bins = 30. Pick better value with binwidth.
<img src="Analysis_files/figure-html/unnamed-chunk-6-14.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-15.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-16.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-17.png" width="672" />
#### Correlations Between Measures
Comparing correlations between the 4 dualex measures
<img src="Analysis_files/figure-html/compare-1.png" width="672" />
#### Flowering
<img src="Analysis_files/figure-html/unnamed-chunk-7-1.png" width="672" />
``` #### Looking at Environment and Height